from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ = None    ,     a__ = None    ,     a__ = False    ,     )      ->tuple[int, float, str]:




  '''simple docstring'''

  _UpperCamelCase           =    cipher_alphabet or [chr(a__ ) for i in range(97    ,     123 )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    _UpperCamelCase           =    {
        "a": 0.08497,
        "b": 0.01492,
        "c": 0.02202,
        "d": 0.04253,
        "e": 0.11162,
        "f": 0.02228,
        "g": 0.02015,
        "h": 0.06094,
        "i": 0.07546,
        "j": 0.00153,
        "k": 0.01292,
        "l": 0.04025,
        "m": 0.02406,
        "n": 0.06749,
        "o": 0.07507,
        "p": 0.01929,
        "q": 0.00095,
        "r": 0.07587,
        "s": 0.06327,
        "t": 0.09356,
        "u": 0.02758,
        "v": 0.00978,
        "w": 0.02560,
        "x": 0.00150,
        "y": 0.01994,
        "z": 0.00077,
    }
  else:
    # Custom frequencies dictionary
    _UpperCamelCase           =    frequencies_dict

  if not case_sensitive:
    _UpperCamelCase           =    ciphertext.lower()

  # Chi squared statistic values
  _UpperCamelCase           =    {}

  # cycle through all of the shifts
  for shift in range(len(a__ ) ):
    _UpperCamelCase           =    ""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        _UpperCamelCase           =    (alphabet_letters.index(letter.lower() ) - shift) % len(
            a__ )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    _UpperCamelCase           =    0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        _UpperCamelCase           =    letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.lower().count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    _UpperCamelCase           =    (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(a__ ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  _UpperCamelCase           =    min(
      a__    ,     key=a__    ,     )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      _UpperCamelCase
  )      ,						(
      _UpperCamelCase
  )      ,						
  )           =    chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ForAudioFrameClassification''',
       '''Wav2Vec2ForCTC''',
       '''Wav2Vec2ForMaskedLM''',
       '''Wav2Vec2ForPreTraining''',
       '''Wav2Vec2ForSequenceClassification''',
       '''Wav2Vec2ForXVector''',
       '''Wav2Vec2Model''',
       '''Wav2Vec2PreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWav2Vec2ForCTC''',
       '''TFWav2Vec2Model''',
       '''TFWav2Vec2PreTrainedModel''',
       '''TFWav2Vec2ForSequenceClassification''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''FlaxWav2Vec2ForCTC''',
       '''FlaxWav2Vec2ForPreTraining''',
       '''FlaxWav2Vec2Model''',
       '''FlaxWav2Vec2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




